The flow of information in a network is often called ‘traffic’. Units of information used in network communication are referred to as ‘packet’. Packets generally arrive at a point in the network at random intervals resulting in ‘bursts’ of traffic resulting in congestion and ‘idle’ periods in which traffic is somewhat more sparse.
Systems that use a network to communicate messages can derive significant benefits from analysis that provides to the system a characterization of the network traffic. The Poisson Process is widely utilized to model aggregate traffic from voice sources. A Markov Modulated Poisson Process (MMPP) is often utilized to model aggregate traffic from data sources. Network traffic has been shown to be self-similar, therefore, a method used to analyze network traffic should be able to display behavior that is bursty and self-similar.
The present method uses a multilevel model that utilizes the model claimed and described in co-pending patent application Docket Number RPS920030018US1 filed concurrently herewith and entitled MMPP ANALYSIS OF NETWORK TRAFFIC USING A TRANSITION WINDOW as the base and replicating the model once for each time-scale displayed by the self-similar traffic. A single level, 2 state MMPP model is shown in FIG. 1.